Analysis of feature categories for malware visualization
It is important to know which features are more effective for certain visualization types. Furthermore, selecting an appropriate visualization tool plays a key role in descriptive,diagnostic, predictive and prescriptive analytics. Moreover,analyzing the activities of malicious scripts or codes is de...
| Main Authors: | , , , , , |
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| Format: | Article |
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Universiti Teknikal Malaysia Melaka
2018
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| _version_ | 1796864059031683072 |
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| author | Samy, Ganthan Narayana Magalingam, Pritheega Mohd. Ariffin, Aswami Fadillah Mohd. Khairudin, Wafa Md. Senan, Mohamad Firham Efendy Yunos, Zahri |
| author_facet | Samy, Ganthan Narayana Magalingam, Pritheega Mohd. Ariffin, Aswami Fadillah Mohd. Khairudin, Wafa Md. Senan, Mohamad Firham Efendy Yunos, Zahri |
| author_sort | Samy, Ganthan Narayana |
| collection | ePrints |
| description | It is important to know which features are more effective for certain visualization types. Furthermore, selecting an appropriate visualization tool plays a key role in descriptive,diagnostic, predictive and prescriptive analytics. Moreover,analyzing the activities of malicious scripts or codes is dependent on the extracted features. In this paper, the authors focused on reviewing and classifying the most common extracted features that have been used for malware visualization based on specified categories. This study examines the features categories and its usefulness for effective malware visualization. Additionally, it focuses on the common extracted features that have been used in the malware visualization domain. Therefore, the conducted literature review finding revealed that the features could be categorized into four main categories, namely, static, dynamic,hybrid, and application metadata. The contribution of this research paper is about feature selection for illustrating which features are effective with which visualization tools for malware visualization. |
| first_indexed | 2024-03-05T20:36:08Z |
| format | Article |
| id | utm.eprints-85514 |
| institution | Universiti Teknologi Malaysia - ePrints |
| last_indexed | 2024-03-05T20:36:08Z |
| publishDate | 2018 |
| publisher | Universiti Teknikal Malaysia Melaka |
| record_format | dspace |
| spelling | utm.eprints-855142020-06-30T08:49:51Z http://eprints.utm.my/85514/ Analysis of feature categories for malware visualization Samy, Ganthan Narayana Magalingam, Pritheega Mohd. Ariffin, Aswami Fadillah Mohd. Khairudin, Wafa Md. Senan, Mohamad Firham Efendy Yunos, Zahri QA75 Electronic computers. Computer science It is important to know which features are more effective for certain visualization types. Furthermore, selecting an appropriate visualization tool plays a key role in descriptive,diagnostic, predictive and prescriptive analytics. Moreover,analyzing the activities of malicious scripts or codes is dependent on the extracted features. In this paper, the authors focused on reviewing and classifying the most common extracted features that have been used for malware visualization based on specified categories. This study examines the features categories and its usefulness for effective malware visualization. Additionally, it focuses on the common extracted features that have been used in the malware visualization domain. Therefore, the conducted literature review finding revealed that the features could be categorized into four main categories, namely, static, dynamic,hybrid, and application metadata. The contribution of this research paper is about feature selection for illustrating which features are effective with which visualization tools for malware visualization. Universiti Teknikal Malaysia Melaka 2018 Article PeerReviewed Samy, Ganthan Narayana and Magalingam, Pritheega and Mohd. Ariffin, Aswami Fadillah and Mohd. Khairudin, Wafa and Md. Senan, Mohamad Firham Efendy and Yunos, Zahri (2018) Analysis of feature categories for malware visualization. Journal of Telecommunication, Electronic and Computer Engineering, 10 (3-2). pp. 1-5. ISSN 2180-1843 https://journal.utem.edu.my/index.php/jtec/article/view/4703 |
| spellingShingle | QA75 Electronic computers. Computer science Samy, Ganthan Narayana Magalingam, Pritheega Mohd. Ariffin, Aswami Fadillah Mohd. Khairudin, Wafa Md. Senan, Mohamad Firham Efendy Yunos, Zahri Analysis of feature categories for malware visualization |
| title | Analysis of feature categories for malware visualization |
| title_full | Analysis of feature categories for malware visualization |
| title_fullStr | Analysis of feature categories for malware visualization |
| title_full_unstemmed | Analysis of feature categories for malware visualization |
| title_short | Analysis of feature categories for malware visualization |
| title_sort | analysis of feature categories for malware visualization |
| topic | QA75 Electronic computers. Computer science |
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